SPADE is a labelled, multi‑modal, simulation‑based dataset for detecting attacks on Signal Phase and Timing (SPaT) messages from the perspective of connected vehicles. It contains 1.89 million timestep records generated by injecting six classes of application‑layer attacks and one benign class into the SAE J2735 SPaT protocol, across multiple intersection geometries, operating conditions, and random seeds. Each record fuses SPaT fields, onboard camera confidence scores, and cooperative V2V peer data into 40 features, enabling deep‑learning intrusion detection systems to distinguish deliberate attacks from environmental noise.
arXiv:2608. 05548v1 Announce Type: cross Abstract: Modern vehicles rely on the Controller Area Network (CAN) bus, whose design prioritizes low cost and real-time performance but provides no message authentication or encryption.
By Chandan Hegde, Mukundh R Reddy
arXiv:2610.07489v1 Announce Type: cross
Abstract: Increasing connectivity to the outside world and the lack of inbuilt security mechanisms have made legacy intra-vehicular networks vulnerable to cybe...
By Shashwat Khandelwal, Shanker Shreejith
arXiv:2606. 18599v1 Announce Type: cross Abstract: The Controller Area Network (CAN) protocol is the primary communication standard for Electronic Control Units (ECUs) in modern vehicles, but its lack of encryption and authentication exposes it to a range of security threats.
By Qiqi Liu, Runhan Song, Lei Cui, Heng Zhang, Yuyan Sun, Limin Sun
The paper presents a digital‑twin (DT) based intrusion detection system (IDS) for vehicle powertrain CAN bus traffic, modeling physical relationships among decoded signals to detect payload‑manipulation attacks that preserve normal timing and sequencing. Using a shared‑encoder LSTM trained on 17 Hyundai/Kia CAN signals, the DT flags anomalies when residuals exceed a threshold, achieving high detection rates (up to 94.6%) for stealthy attacks such as continuous drift and masquerade, while a range‑and‑plausibility baseline fails to detect them. The study demonstrates that learning coupled vehicle dynamics enables detection of payload‑level attacks that evade traditional timing‑based IDSs, though false positives remain a challenge.
By Araf Rahman, M Sabbir Salek, Mashrur Chowdhury
arXiv:2606. 28625v1 Announce Type: cross Abstract: Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety.
By Mohammad Imtiaz Hasan, Abyad Enan, Jean Michel Tine, Araf Rahman, M Sabbir Salek, Mashrur Chowdhury
arXiv:2606. 30430v1 Announce Type: cross Abstract: The increasing connectivity of modern vehicles has made securing in-vehicle communication networks a critical challenge.
By Beatrix Koltai, Gergely Acs, Andras Gazdag
Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency, or sequencing and cannot detect attacks that pre...
arXiv:2512. 15503v3 Announce Type: replace-cross Abstract: Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everything (V2X) communication.
By Konstantinos Kalogiannis, Ahmed Mohamed Hussain, Hexu Li, Panos Papadimitratos
arXiv:2607. 16175v1 Announce Type: cross Abstract: Connected and Autonomous Vehicles (CAVs) rely on interconnected software and hardware components, including sensors, Electronic Control Units, in-vehicle infotainment systems, and telematics units, where vulnerabilities can compromise assets, users, and vehicle operations.
By Md Erfan, Ahmed Ryan, Md Kamal Hossain Chowdhury, Md Rayhanur Rahman
arXiv:2609.17856v1 Announce Type: new
Abstract: Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, wh...
By Chenyi Wang, Yutong Liu, Qingzhao Zhang, Ming F. Li
By leveraging data from video-based perception systems, intelligent transportation systems (ITS) support safety-critical applications that improve road safety. However, adversaries may manipulate video frames to compromise downstream perception modules, causing failures in safety-critical functions and increasing risks to vulnerable road users.